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Genkit MCP

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by firebase
hello.py2.19 kB
# Copyright 2025 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # SPDX-License-Identifier: Apache-2.0 from genkit.ai import Genkit from genkit.plugins.dev_local_vectorstore import DevLocalVectorStore from genkit.plugins.google_genai import VertexAI from genkit.types import Document ai = Genkit( plugins=[ VertexAI(), DevLocalVectorStore( name='films', embedder='vertexai/text-embedding-004', ), ], model='vertexai/gemini-2.0.', ) films = [ 'The Godfather is a 1972 crime film directed by Francis Ford Coppola.', 'The Dark Knight is a 2008 superhero film directed by Christopher Nolan.', 'Pulp Fiction is a 1994 crime film directed by Quentin Tarantino.', "Schindler's List is a 1993 historical drama directed by Steven Spielberg.", 'Inception is a 2010 sci-fi film directed by Christopher Nolan.', 'The Matrix is a 1999 sci-fi film directed by the Wachowskis.', 'Fight Club is a 1999 film directed by David Fincher.', 'Forrest Gump is a 1994 drama directed by Robert Zemeckis.', 'Star Wars is a 1977 sci-fi film directed by George Lucas.', 'The Shawshank Redemption is a 1994 drama directed by Frank Darabont.', ] @ai.flow() async def index_documents() -> None: """Indexes the film documents in Firestore.""" genkit_documents = [Document.from_text(text=film) for film in films] await DevLocalVectorStore.index('films', genkit_documents) print('10 film documents indexed successfully') @ai.flow() async def retreive_documents(): return await ai.retrieve( query=Document.from_text('sci-fi film'), retriever='films', ) ai.run_main()

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